The Role of Time Series Analysis in Stock Market Prediction

Jiali Shi ()
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Jiali Shi: University of California

A chapter in Proceedings of the 2024 3rd International Conference on Public Service, Economic Management and Sustainable Development (PESD 2024), 2024, pp 329-334 from Springer

Abstract: Abstract This study explores the application of time series analysis in predicting stock market trends, focusing on the ARIMA (AutoRegressive Integrated Moving Average), GARCH (Generalized Autoregressive Conditional Heteroskedasticity), and LSTM (Long Short-Term Memory) models. These models have been selected for their unique capabilities in capturing different aspects of market behavior, from linear trends to volatility clustering and complex temporal dependencies. Through a comprehensive literature review and comparative case study analysis, this research evaluates the effectiveness of these models in various market environments, particularly in emerging markets. The findings suggest that while classical models like ARIMA and GARCH are effective for short-term predictions, integrating them with modern machine learning techniques such as LSTM can significantly enhance prediction accuracy and robustness. This study contributes to the ongoing development of more sophisticated forecasting tools, offering practical insights for investors and financial analysts in optimizing their decision-making processes.

Keywords: Time Series Analysis; Stock Market Prediction; ARIMA; GARCH; LSTM (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-598-0_34

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DOI: 10.2991/978-94-6463-598-0_34

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